Machine Learning Engineer - Marketplace Growth (TikTok Global E-commerce Supply Chain and Logistics)

$136,800 - $280,000/Yr

Tiktok - San Jose, CA

Full-time - Mid Level

San Jose, CA

Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services

About the position

TikTok is the leading destination for short-form mobile video, and our mission is to inspire creativity and bring joy. The E-Commerce Supply Chain and Logistics team is dedicated to enhancing clients' shopping experience and reducing operational costs in the supply chain and logistics of TikTok E-commerce. We achieve this by developing end-to-end algorithm capabilities using machine learning, operations research, data mining, and causal inference methods. We are seeking a talented and motivated Machine Learning Engineer with expertise in marketplace growth to join our dynamic and fast-paced team. In this role, you will collaborate with cross-functional teams including data scientists, engineers, product managers, and business stakeholders to develop innovative solutions that unlock the value hidden within our data, leading to improved decision-making and operational efficiencies. As a Machine Learning Engineer, you will design and develop machine learning algorithms for various tasks, support marketplace growth business goals, collaborate with experts to define data mining objectives, apply feature engineering techniques, evaluate and benchmark different machine learning approaches, and communicate findings effectively to both technical and non-technical stakeholders. Staying updated with the latest advancements in data mining and machine learning will be crucial to enhance the team's capabilities and identify new opportunities.

Responsibilities

• Design and develop machine learning algorithms for various tasks, including customer segmentation, scoring, outreach, marketing, characterization, and incentive optimization.
• Support marketplace growth business goal attainment through optimizing outreach based on click-through rate estimation, outreach timing selection, target audience selection, and causal studies.
• Collaborate with data scientists, analysts, and subject matter experts to define data mining objectives and develop strategies to address complex business problems and opportunities.
• Apply feature engineering techniques to derive relevant features and embeddings from raw data and improve the performance of data mining models.
• Evaluate and benchmark different machine learning approaches, algorithms, and tools, and recommend the most appropriate solutions based on performance, scalability, and interpretability.
• Stay updated with the latest advancements in data mining, machine learning, and related fields, and apply this knowledge to enhance the team's capabilities and identify new opportunities.
• Communicate findings, insights, and technical concepts effectively to both technical and non-technical stakeholders, fostering a collaborative and data-driven decision-making culture.

Requirements

• Master's or advanced degree in Computer Science, Data Science, Statistics, or a related field.
• 3+ years experience as a Machine Learning Engineer, Data Scientist, and experience in Deep Learning is preferred.
• Work experience in user growth, marketing algorithms, recommendation algorithms, advertisement algorithms or related fields is preferred.
• Proficient in using SQL + Python and experience with data manipulation and analysis libraries, experience with TensorFlow or PyTorch is preferred.
• Experience with big data processing frameworks (e.g., Hadoop, Spark) and distributed computing for efficient data mining on large-scale datasets.
• Solid understanding of machine/deep learning concepts and techniques, including feature engineering, model evaluation, and optimization.
• Strong analytical and problem-solving skills, with a demonstrated ability to handle and derive insights from complex and unstructured datasets.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and convey technical concepts to non-technical stakeholders.
• Publications at KDD, NeurIPS, WWW, SIGIR, WSDM, CIKM, ICLR, ICML, IJCAI, AAAI and related conferences.

Nice-to-haves

• Experience in deep learning techniques and frameworks.
• Familiarity with causal inference methods and their application in marketplace growth.
• Knowledge of marketing strategies and their impact on user engagement.

Benefits

• 100% premium coverage for employee medical insurance, approximately 75% premium coverage for dependents.
• Health Savings Account (HSA) with a company match.
• Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life and AD&D insurance plans.
• Flexible Spending Account (FSA) Options like Health Care, Limited Purpose and Dependent Care.
• 10 paid holidays per year plus 17 days of Paid Personal Time Off (PPTO) and 10 paid sick days per year.
• 12 weeks of paid Parental leave and 8 weeks of paid Supplemental Disability.
• Mental and emotional health benefits through EAP and Lyra.
• 401K company match, gym and cellphone service reimbursements.